Demand Validation

Why this matters for your decision

Many decisions are made on an assumption about demand: how many people will take out a new tariff, whether a premium price will hold, whether a feature justifies the extra charge. This assumption flows into the business case, volumes and budget. If it is wrong, everything built on it is wrong.

How high the risk of failure really is tends to be exaggerated: the common figure of 80% of new products failing does not stand up to scrutiny, and empirical studies find 40% or less. The risk is still large enough to check demand before the investment rather than after it.

How to validate demand

There are three ways. First, stated intention: surveys, concept tests, conjoint. Fast and good for narrowing things down, but prone to people deciding differently in a questionnaire than in everyday life. Second, observed behaviour before launch: Painted Door Tests and other forms of Pretotyping. Third, behaviour after launch: sales data, A/B tests, regional roll-out. The most accurate, but too late to still steer the investment.

Horizon works in the second field. Real consumers see a realistic offer via Google and Meta ads and decide whether to choose it. Nothing is sold, and the reveal follows immediately. This produces measured purchase intent before anything is built, produced or priced.

Example

An energy supplier is planning a rental package of a balcony solar system and battery storage. The question: is demand large enough compared with the existing purchase offer? In the test, both offers run side by side with neutral design, and the familiar purchase offer serves as the benchmark.

Result: the rental package achieves a measured sign-up intent of 1.6%, the purchase offer 1.2%. Because the actual sales figures of the purchase offer are known, the ratio can be translated into volume scenarios.

How it differs

Demand validation describes the goal; Pretotyping and the Painted Door Test describe methods for reaching it. Problem validation checks whether a need exists; demand validation checks whether a concrete offer with a price is chosen. Market sizing estimates work with secondary data; demand validation delivers a data point of its own from the target group.

Horizon does not see demand validation as a one-off step before launch, but as a repeatable check at the points where budget is committed: before development, before setting the price, before launching in another market.

Limitations

A single demand value without comparison is hard to read: 2% says little as long as it is unclear whether that is a lot or a little. That is why every demand validation needs a benchmark, such as an existing offer of your own or comparable reference values.

What is measured is the first decision in the online environment. The test does not capture repeat purchase, usage, sales through retail or field sales, or competitor reactions. The translation into sales volumes remains a scenario, not a certainty.